7.逻辑回归实践

import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import classification_report
from sklearn.metrics import mean_squared_error


# 逻辑回归

def logistic():
    # 分类
    column = [‘数据编号‘, ‘属性1‘, ‘属性2‘, ‘属性3‘, ‘属性4‘, ‘属性5‘, ‘属性6‘, ‘属性7‘, ‘属性8‘, ‘属性9‘, ‘类别‘]
    # 读取数据
    data = pd.read_csv(‘C:\\Users\\86186\\Desktop\\大三下\\机器学习\\breast-cancer-wisconsin.csv‘, names=column)

    # 缺失值处理
    data = data.replace(to_replace=‘?‘, value=np.nan)
    data = data.dropna()

    # 数据分割
    x_train, x_test, y_train, y_test = train_test_split(data[column[1:10]], data[column[10]], test_size=0.3)

    # 特征值和目标值进行标准化处理（分别处理）
    std = StandardScaler()
    x_train = std.fit_transform(x_train)
    x_test = std.transform(x_test)

    # 逻辑回归预测
    lg = LogisticRegression()
    lg.fit(x_train, y_train)
    print(lg.coef_)
    lg_predict = lg.predict(x_test)
    print(‘准确率:‘, lg.score(x_test, y_test))
    print(‘召回率:‘, classification_report(y_test, lg_predict, labels=[2, 4], target_names=[‘良性‘, ‘恶性‘]))


if __name__ == ‘__main__‘:
    logistic()